In the context of data stores, consistency refers to the concept that data written to a data store should comply with defined rules for valid data. A defined rule for data may specify allowed ways that a data transaction may affect data stored in a data store or database. If a data transaction attempts to introduce inconsistent data by violating a defined rule, the data transaction may be rolled back, blocked, or aborted, and an error may be returned to a user. As an example, a data consistency check may include a consistency rule specifying that a day of the week column contain non-abbreviated values. If a data transaction attempts to write an abbreviated value to the column (e.g., “Tues”), then the data consistency check may cause the data transaction to be rejected.
The advent of centralized computing service architectures has opened new possibilities for managed data store services. In general, a centralized computing service architecture may deploy a set of hosted resources such as processors, operating systems, software and other components that can be combined together to form virtual resources. These virtual resources may be used to provide various managed data store services, such as relational and nonrelational data stores. A customer can deploy a data store in a service provider environment, and a managed data store service may automatically scale throughput capacity to meet workload demands. Also, the data store service may manage aspects of the data store that include partitioning and repartitioning customer data as data store tables grow, synchronously replicating data across data centers, as well as providing built-in security, backup and restore functionality, and in-memory caching.